System and method for tracking users across a plurality of media platforms

ABSTRACT

A system and method for tracking users across a plurality of media platforms are provided. The method includes generating unified user-level data of each user across the media of advertising platforms; storing the unified user-level data generated for each user in a storage; taping into the plurality of advertising platforms to render pixel trafficking data; and analyzing raw data based on which the unified user-level data is generated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 14/807,995 filedJul. 24, 2015. The Ser. No. 14/807,995 application is acontinuation-in-part (CIP) of U.S. patent application Ser. No.14/077,951 filed on Nov. 12, 2013, now pending, which claims the benefitof U.S. provisional patent application No. 61/752,594 filed Jan. 15,2013, the contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to harnessing big data gatheredfrom multiple data sources, and more particularly to analyticmeasurements across multiple data sources.

BACKGROUND

Techniques for collecting, managing, and providing real-time or nearreal-time relevant information have been enhanced through the use of theInternet and online research and information collection tools. One suchset of tools is known as web analytics. Web analytics focuses on acompany's own website for collection of online information, particularlytraffic data. Web analytics are limited in that they only consider asubset of the relevant online universe, specifically the behavior ofusers of a given website.

Other analytics tools try to learn and predict the exposure and reach ofadvertisements displayed on websites, including social media websites.These tools gather statistics related to the reach and exposure of theadvertisements. The statistics may include the number of impressions,URLs of webpages displaying the advertisements, geographical locationsof users that watched the advertisements, click-through rate ofadvertisements, the period of time that each viewer watched theadvertisements, and so on.

Currently, every ad-serving company as well as each social media websiteindependently gathers its own statistics and analytics with regard tothe exposure and reach of advertisements. However, campaign managers wholike to have better understanding about the reach of advertisements andwhether their budget was well spent have limited tools by which to doso. As a result, campaign managers cannot efficiently analyze andunderstand the performance of an advertisement campaign.

Specifically, the information gathered by a single ad-serving company ora social website per campaign may include trillions of records.Multiplying these by different companies serving the same campaignsmakes it almost impossible for campaign managers to analyze the gatheredinformation using existing tools. Further, in addition to the volume ofthe gathered information, each ad-serving company presents the gatheredstatistics using a different format. This further increases thecomplexity of the campaign analysis.

It should be noted that failing to efficiently and accurately analyzethe performance of an advertising campaign results in revenue losses forbusinesses, as their advertising budget is not being efficiently spent.

Additionally, existing user level database solutions typically utilizecookies (or any type of identifiers) received from each ad-servingcompany (such as, e.g., a social media website, ad-serving systems, andthe like) to determine user identities. Each ad-serving company orwebsite normally uses its own unique identifier (user ID) to mark theend user. As a result, it is probable that the same end-user accessingadvertisements via multiple ad-serving companies and/or social mediawebsites can be mapped to numerous users in the user level database.This multiple mapping can create misleading data, thereby resulting inloss of information or conspicuous inconsistencies in data.

As an example, a user may view an advertisement for Coca Cola® on bothFacebook® and Twitter®. The Coca Cola® company may wish to determine thereach of its advertising campaign by determining how many users viewedits campaigns across various media platforms. With respect to this user,Facebook® and Twitter® have stored different user IDs (e.g., in a formof cookies) for the same user. When Coca Cola® seeks to generate a userlevel database to track how many people have viewed its advertisingcampaign, that user may be marked in the user level database twice. Whenthis scenario occurs respective of many users, the data ceases to betruly reflective of the number of people who actually viewed thecampaign.

Moreover, the inconsistency in the application level user data wouldprevent campaign managers from deriving accurate and meaningfulanalytics respective of their campaigns. For example, post-impression orpost-conversion data can be analyzed. As another example, campaignmanagers cannot properly assess the effectiveness of each of the mediaplatform campaign running the campaign.

It would therefore be advantageous to provide a solution that wouldovercome the deficiencies of the prior art by generating unified userlevel data across a variety of different media platforms.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor delineate the scope of any orall embodiments. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term someembodiments may be used herein to refer to a single embodiment ormultiple embodiments of the disclosure.

The disclosure relates in some embodiments to a system for trackingusers across a plurality of media platforms, comprising: a storage; aprocessor; and a memory, the memory containing instructions that, whenexecuted by the processor, configure the system to: generate unifieduser-level data of each user across the media of advertising platforms;store the unified user-level data generated for each user in a storage;tap into the plurality of advertising platforms to render pixeltrafficking data; and analyze raw data based on which the unifieduser-level data is generated.

The disclosure relates in various embodiments to method for trackingusers across a plurality of media platforms. The method comprisesgenerating unified user-level data of each user across the media ofadvertising platforms; storing the unified user-level data generated foreach user in a storage; taping into the plurality of advertisingplatforms to render pixel trafficking data; and analyzing raw data basedon which the unified user-level data is generated.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a schematic block diagram of a system for cross-platform bigdata analytics utilized to describe the various disclosed embodiments.

FIG. 2 is a flowchart illustrating a method for providing cross-platformanalytics according to one embodiment.

FIG. 3 is a schematic block diagram of a media-link module implementedaccording to one embodiment.

FIG. 4 is a diagram illustrating the operation of the media-link moduleaccording to an embodiment.

FIG. 5 is a flowchart illustrating a method for generating unified userlevel data according to one embodiment.

FIG. 6 is a diagram illustrating tracking post-impression and post-clickconversion according to one embodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

FIG. 1 shows an exemplary and non-limiting block diagram of a system 100for cross-platform big data analytics utilized to describe the variousembodiments. The system 100 includes a data sanitizing module 102, atransformation and storage (TS) engine 104, a data mart module 106, ametadata database (DB) 108, a management user interface (UI) module 110,and a media-link module 112.

The data sanitizing module 102 is configured to load data into thesystem 100 and to produce a dataset normalized to a predefined unifiedformat. That is, regardless of the format or the type of the input data,the output of the data sanitizing module 102 is a data set in a unifiedformat. The input data uploaded to the data sanitizing module 102 maybe, but is not limited to, unstructured data, structured data,standardized data (e.g., Excel, XML, etc.), and so on.

The data sanitizing module 102 is configured to support both push andpull operations facilitated through a plurality of data transferprotocols. Such protocols include, for example, FTP, SFTP, FTPS, HTTP,HTTPS, SMTP, POP3, and the like. According to one embodiment, the datasanitizing module 102 is also configured to decrypt the data if suchdata is received in an encrypted form. The decryption keys are providedby the entity owning the data.

In another embodiment, the data sanitizing module 102 is configured toidentify and associate the incoming data with the entity owning thedata. Such an entity may be, for example, a public relations enterpriserunning the advertisement campaign, an advertising agency, a campaignmanager, and so on. The processing of the data identification and itscorresponding association is required in order to ensure the security ofsuch data in the system 100. That is, the processing is required toensure that data related to one entity is not shared with or utilized byother entities using the system 100.

In one embodiment, the data sanitizing module 102 includes a scheduler(not shown) configured to pull data from pre-integrated API-based datasources. The data sanitizing module 102 may further include a listener(not shown) for determining if the data is ready to be uploaded to thesystem 100. The listener is configured to perform any one of the filetransfer protocols supported by the data sanitizing module 102 such as,e.g., FTP, SFTP, FTPS, HTTP, HTTPS, SMTP, POP3, and the like.

The TS engine 104 is a non-transitory data repository for the normalizeddata provided by the data sanitizing module 102 or the media-linktracking and media-link module 112. The TS engine 104 is configured totransform the normalized dataset into a relaxed user-specific dataschema. The relaxed data schema includes the data types, dimensions,metric definition, hierarchy, and aggregation function for each metric.Thereafter, the TS engine 104 is configured to execute a datatransformation process to transform data values in the dataset to meetthe relaxed data schema. The data transformation is performed by aplurality of transformation rules. This transformation results in adataset (hereinafter the “relaxed dataset”) that includes relevant datagathered from multiple platforms, organized according to the relaxeddata schema as specified by the user.

The TS engine 104 is further configured to analyze the relaxed datasetto compute various campaign measurements of measurable data itemsincluded in the relaxed dataset. The analysis performed by the TS engine104 includes data aggregation, and analytical as well as statisticalcalculations. For example and without limitation, the statisticalmeasurements for each such data item include an average, a normaldistribution, a maximum value, a minimum value, an absolute value, andso on. A measurable data item is any item that that can be aggregated.For example, currency values, conversion rates, a number of hits, anumber of clicks, a number of fans, a number of page views, and a numberof leads are merely a few examples of measurable data items.

In accordance with another embodiment, the various measurements aregenerated with respect to one or more campaign objectives defined by theuser or preconfigured in the system 100. For example, if the campaignobjective is to reach 100,000 fans in a social network, the TS engine104 is configured to compute the current number of fans and the rate ofnew fan acquisition, and predict whether the campaign objective can bemet and when. Finally, the TS engine 104 is configured to populate theanalyzed data and/or the resulting dataset into the data-mart module106. It should be noted that the aggregation of the calculationperformed by the TS engine 104 allows retrieving the processedinformation by the UI module 110 without latency. That is, the datasetis preprocessed without waiting for a specific query. The variousprocesses performed by the TS engine 104 are discussed in greater detailwith reference to FIG. 2.

In one embodiment, the data saved in the data-mart module 106 isoptimized for providing fast access to the data. This allows producingreports, answering queries, and/or receiving the relevant portions ofthe aggregated data on the fly without any delay. The data mart module106 is optimized for high concurrency, scalability and availability.

In another embodiment, the TS engine 104 is also configured to store thedata mapped to the destination schema in the data warehouse 130 forlater usage. This may include, for example, custom querying,service-based analysis (e.g., analysis performed by a Data Scientistteam) and re-processing of the stored data.

The data warehouse 130 may be communicatively connected to the system100 or integrated therein. The data warehouse 130 is accessed throughthe data mart module 106 which is configured to allow acceleratedretrieval of the aggregated data stored in the data warehouse 130. Inone embodiment, the data-mart module 106 is realized as a data structureserver.

The metadata DB 108 is configured to store and maintain metadatautilized by the system 100, and in particular by the TS engine 104 forprocessing and analyzing of campaign data. The metadata DB 108 may beintegrated in the system 100 (as shown in FIG. 1) or communicativelyconnected thereto. In one embodiment, the metadata DB 108 is realized asan online transaction processing (OLTP) database which is configured tosupport the various processes performed by the system 100.

The management UI module 110 is configured to provide access to thesystem 100 from various client devices. The client devices may include,for example, a PC, a smart phone, a tablet computer, and the like. Thecommunication with the management UI module is facilitated through anapplication executed over the client device; such an application mayinclude a web browser. In one embodiment, the management UI module 110implements a set of application programming interfaces (API) to allowcommunication with the client device.

In an embodiment, the TS engine 104 can analyze data provided by thedata sanitizing module 102, where such data is typically loaded into thesystem 100 “off-line”. That is, the data sources connected to the module102 provide data as gathered, over time, from different advertisingplatforms. As such, the data sources are adapted to upload or “push”data to the system 100 as the campaign analytics are published by eachrespective advertising platform.

According to another embodiment, the TS engine 104 can analyze“real-time” data collected by the media-link module 112 with regard toone or more online campaigns. The media-link module 112 is configured totap into advertising platforms and to track their entire media plans.The media plan is typically defined by a media agency and entails mediaplatforms for the campaign. The media plan is designed to find thespecific combination of media to best achieve the marketing campaignobjectives.

Therefore, the media-link module 112 is also configured to gather otherdata related to advertising campaigns in real time, when such data ispublished and/or collected by an advertising platform. The data gatheredby the media-link module 112 is input to the data sanitizing module 102.An advertising platform may be an ad-serving system of an ad-servingcompany, a social media website, a content publisher, and the like.Various components of a typical media-link module 112 are describedfurther herein below with respect to FIG. 3.

Each, some, or all of the modules of the system 100 may be realized by aprocessing system. The processing system may comprise or be a componentof a larger processing system implemented with one or more processors.The one or more processors may be implemented with any combination ofgeneral-purpose microprocessors, microcontrollers, digital signalprocessors (DSPs), field programmable gate array (FPGAs), programmablelogic devices (PLDs), controllers, state machines, gated logic, discretehardware components, dedicated hardware finite state machines, or anyother suitable entities that can perform calculations or othermanipulations of information.

The processing system may also include machine-readable media forstoring software. Software shall be construed broadly to mean any typeof instructions, whether referred to as software, firmware, middleware,microcode, hardware description language, or otherwise. Instructions mayinclude code (e.g., in source code format, binary code format,executable code format, or any other suitable format of code). Theinstructions, when executed by the one or more processors, cause theprocessing system to perform the various functions described herein.

FIG. 2 shows an exemplary and non-limiting flowchart 200 illustrating amethod for providing cross-platform analytics using the systemdemonstrated in FIG. 1. The method is performed by the data sanitizingmodule 102 and TS engine 104 of the system 100.

At S210, data gathered by a plurality of advertising platforms regardingone or more advertising campaigns is uploaded to the data sanitizingmodule 102. The uploaded data may be received from ad-serving companies,social media websites, advertisement agencies, and the like. Thereceived data may be provided to the data sanitizing module 102 ineither pull or push operation modes.

As noted above, the data sanitizing module 102 supports a plurality ofprotocols for communication with the data sources and platforms forreceiving data. In one embodiment, input data may be in any data format,such as structured, unstructured, and standardized (e.g., Excel, XML,and the like). For the sake of simplicity and without limiting the scopeof the disclosed embodiments, the data may be uploaded in the form offiles.

Optionally, at S215 the received data is pre-processed. Thispre-processing includes, but is not limited to, decryption of datareceived in an encrypted form. With this aim, the data sanitizing module102 is configured to maintain or receive over a secured connection therelevant encryption keys from the data owner. In one embodiment, S215further includes identification and association of each input data filewith its respective data owner entity.

At S220, each input data file is parsed to filter out irrelevantinformation contained in the file. As a non-limiting example, an Excelfile is parsed to identify blank rows and to remove such rows from thefile. As another non-limiting example, headers and commentaryinformation are filtered out as well.

At S221, data dimensions (fields) in the input file are mapped to across-platform marketing data model. This cross-platform marketing datamodel is designed according to the disclosed embodiments to supportmarketing and analytical metrics of online advertising. In an exemplaryembodiment, the cross-platform marketing data model defines at least adate dimension and at least one metric dimension. The at least onemetric dimension may be predefined in the system 100 and customized bythe user. Examples for such metric dimensions include, but are notlimited to, impression counts, click counts, conversion, media cost,placement, and so on. The data model may also define dimensions such ascurrency, geographical location, campaign name, a default value, and thelike. The cross-platform marketing data model further defines therelationship between objects, their hierarchies, their data type, andtheir format. It should be noted that the system 100 may bepreprogrammed with the cross-platform marketing data model which may befurther customized by a user of the system.

The mapping of dimensions (fields) in the input file to thecross-platform marketing data model includes analyzing the input file todetermine a data type of each field and field name, matching between asource field name and a dimension in the model based in part on the datatype and the field name. For example, if a source field name in theinput file is “clicks”, the source field name can be mapped to a metricdimension “delivery clicks” in the predefined data model. The mapping isperformed for each dimension or for a predefined set of dimensions inthe input file. Data entries that cannot be mapped to the dimensions inthe cross-platform marketing data model are placed in the default valuedimensions. The result of S221 is a dataset being organized andfunctioning as defined in the cross-platform marketing data model.

At S222, data values in the dataset are normalized to be represented ina unified notation. In one embodiment, data values of common datadimensions are normalized. For example, common data dimensions arerelated to data entries likely to appear in most of the files input tothe data sanitizing module 102. Examples for common data dimensionsinclude, but are not limited to, date, currency, country, zip code, andso on. The data normalization may include, for example, representing adate dimension (field) in a notation of ‘MM/DD/YYYY’, convertingcurrency to USD (using a current exchange rate), representing a countrycode using 2 characters, and so on. The unified notation is determinedby the format of the respective dimension as defined in thecross-platform marketing data model. The result of S222 is a normalizeddataset being organized and functioning as defined in the cross-platformmarketing data model.

At S223, a check is made to determine if all input files have beenprocessed and, if so, execution continues with S224; otherwise, a newinput file is selected and execution returns to S215. The executionreaches S224 when data contained in all the input files are aggregatedin the normalized dataset and organized to function as defined in thecross-platform marketing data model. At S224, the normalized dataset isfurther optimized to allow faster manipulation of the data. In oneembodiment, the optimization includes saving the dataset in acolumn-based format. It should be noted that, during the manipulationand organization of input data files, data is saved in a storage device,which may be a database, the data warehouse 130, and the like.

At S230, a relaxed data schema is attached to the normalized dataset.The relaxed data schema includes data types, dimensions, metricdefinitions, a hierarchy of data fields, and an aggregation function foreach metric. In an embodiment, the relaxed data schema determines howdata values from the normalized dataset will be read and manipulated.The relaxed data schema is user-specific, i.e., it is defined based onthe user's requirements.

At S231, data values in the normalized dataset are transformed to complywith the relaxed data schema. The data transformation is performed bymeans of a plurality of transformation rules. A transformation ruledefines at least one of the following actions to be performed on thedata: alteration, classification, and segmentation. For example, thesegmentation rule may define that all impressions counted during aweekend will be grouped together. As another example, personalinformation recorded in one record in the following notation:‘first-name_last-name_age_gender’ is expanded into different individualattributes, such as ‘first name’, ‘last name’, ‘age’, and ‘gender’. Thisallows aggregating and performing analytic functions on each attributeindividually. The system 100 through the UI management module 110 allowsthe user to define transformation rules. S231 results in a modifieddataset that meets the definitions of the relaxed data schema. At S232,the data transformed to comply with the relaxed data schema is saved inthe data warehouse 130 for later usage.

At S233, the modified dataset is analyzed to provide measurements on theaggregated data. In one embodiment, the analysis includes aggregationand analytical calculations across all measurable data items in themodified dataset and/or with respect to campaign objectives. Thestatistical measurements include, but are not limited to, an average, anormal distribution, a maximum value, a minimum value, an absolutevalue, and so on. A measurable data item is any item that can beaggregated. For example, currency values, conversion rates, a number ofhits, a number of clicks, a number of fans, a number of page views, anda number of leads are merely a few examples for measurable data items.Additional examples are provided above.

At S234, the results of the analysis are saved in the data-mart module106. That is, the computed measurements are saved as part of the datasetor in association with the dataset. The data-mart module 106 isconfigured to adapt the data received from the TS engine 104 into aformat that is accessible and query-able by the UI management module110.

FIG. 3 is an exemplary and non-limiting schematic block diagram of amedia link module 112 implemented according to one embodiment. In anexemplary implementation, the media link module 112 includes a unifiedtracking module 114, a database 116, a trafficking manager 118, and adata aggregator 119.

In an embodiment, the unified tracking module 114 provides pixeltracking services for mapping users of different media platforms.Examples for such platforms include, but are not limited to, socialmedia channels (or websites), webpages, ad-serving systems, customerrelationship management (CRM) systems, and the like. The unifiedtracking module 114 is further configured to provide unified usertagging across the different media platforms. In an embodiment, theunified tracking module 114 handles HTTP/HTTPS calls generated by clientdevices. Each client device (or user) is assigned a unique user-ID andthe device's activity is logged in the unified tracking server's 114 logfile. Thereafter, those log files are loaded to the TS engine 104. Usersof client devices include viewers viewing advertisements displayed onwebpages, mobile applications, online games, and the like.

The database 116 is configured to store a server side data-structure. Inone embodiment, the server side data-structure is an extension of anHTTP cookie mechanism. In another embodiment, the server sidedata-structure is utilized as an HTTP cookie mechanism in environmentsthat do not support such cookies (e.g., IPTV, Mobile). The database 116stores the unified user-level data which is in a persistent user statein a scalable manner.

The trafficking manager 118 is configured to perform a pixel traffickingservice that taps into the different media platforms, and in particularad-serving systems, to enable tracking of their media plans. A trackingpixel is a tag installed or placed in an advertising platform. The tagmay be in a form of an HTML tag, a JavaScript file, and the like. Thetracking pixel calls upon another service to provide the media-linkmodule 112 with analytics information gathered from the advertisingplatform.

In one embodiment, the trafficking manager 118 included in the medialink module 112 allows for automatically placement of a proprietarytracking tag on top of an existing media plan on various ad-servingplatforms. This allows combining user level data from multiplead-serving platforms.

In a non-limiting embodiment, the trafficking manager 118 is configuredto connect to a media platform (e.g., an ad-serving server) through anAPI, extract the media plan, and learn the different entities of theextracted media plan. Then, the extracted media plan is mapped to apredefined media plan structure utilized by the system 100. Once thetrafficking manager 118 learns the structure of the extracted mediaplan, the trafficking manager 118 is configured to create conversiontags and to update the media plan of the respective platform to call upthe proprietary conversion tags (e.g., through the API).

In an embodiment, the trafficking manager 118 dynamically learns of thechanges made in the media plans of previously tagged advertising mediaplatforms. With this aim, the trafficking manager 118 is configured toperiodically connect to the advertising media platforms, and for eachplatform, the trafficking manager 118 is configured to compare itsexisting snapshot of the media plan with a current media plan of arespective platform and to make the necessary adjustments.

The data aggregator 119 is configured to receive raw data from theunified tracking server 114. The raw data may be in a serialized andoptimized data format. The data aggregator 119 is configured to validateand aggregate the raw data into multiple aggregations and analyticalcomputations, such as summing up events per hour per location,calculating overlap between different channels, and so on. Theaggregated data is input to a data sanitizing module (e.g., the datasanitizing module 102). In one embodiment, the data processed by thedata aggregator 119 can be stored for future usage, such as customquerying and data analytics services.

Each, some, or all of the elements of the media-link module 112 may berealized by a processing system. The processing system may comprise orbe a component of a larger processing system implemented with one ormore processors.

The operation of the media link module 112 is further described in FIG.4. The media link module 112 can interface with a plurality ofadvertising media platforms 401 through 405. As illustrated in FIG. 4,these platforms include third party data providers 401, ad servers 402,advertiser websites 403, media planners 404, and end-users 405. Forexample, the ad servers 402 may include social network channels (e.g.,Facebook®), ad-serving serving companies, and the like. The advertiserwebsites 403 are often websites accessible through a landing page. Userscan reach advertiser websites 403 upon, e.g., clicking an advertisementdisplayed on a publisher website. The third party data providers 401 aresystems of companies that collect and share information about userssurfing the web. The media planners 404 can control, set, or configurethe operation of the media link module 112. In an embodiment, mediaplanners 404 also have access to the unified user-level data and anytype of data generated and/or aggregated by the media link module 112.

In an embodiment, the media link module 112 is configured to perform aunified tracking process in user-level data from the different adservers 402 and advertiser websites 403 are tracked and combined in aunified user-level data. The media link module 112 can track and analyzeuser-level data in any format provided by the various ad servers 402 andadvertiser websites 403 to generate the unified user-level data. In anembodiment, the user-level data tracked by the media link module 112includes conversion tags. The conversion tags may be propriety tags. Aswill be discussed in detail below, the conversion tags allow the medialink module 112 to track post-impression and post-click conversions onthe advertiser websites 403.

The tracking of user level data is performed at different granularitiesfor different platforms 402 and 403. For the ad servers 402, the entiremedia plan is tracked. As noted above, the media plan is designed tofind the specific combination of media to best achieve the marketingcampaign objectives. For the advertiser website 403, a user activity andconversions are tracked. It should be noted that in both casesuser-level data gathered or retrieved from the data providers 401, adserver 402, and/or advertiser websites 403 are analyzed. Each instanceof a user-level data is referred to as event. Typically, user-level dataas received by these platforms include one or more of the followingfields: a timestamp, a user ID, an IP address associated with a clientdevice of the user, a user agent (e.g., web browser type), and an eventparameter. Each platform has its unique representation of the user-leveldata and a particular way to code the userID.

For example, if the ad-servers are Facebook® and Google® and theadvertiser website is Nike®, their respective user-level data is ULD₁,ULD₂, and ULD₃, where ULD₁, ULD₂, and ULD₃ represent different data forthe same user. In order to provide consistent user-level data that canbe mapped or associated to the particular user, the media-link module112 unifies ULD₁, ULD₂, and ULD₃, into a unified user-level identifier(ULD_(G)) which is a function of the data received from the differentplatform. That is:

-   -   UDL_(G)=f{ULD₁, ULD₂, and ULD₃}

It should be noted the UDL_(G) cannot be recognized by the variousplatforms, as it does not comply with their format. The process forunifying the user-level data received from different sources isdescribed below with reference to FIG. 5.

In an embodiment, the unified user-level data is generated per user(identified by a user ID) per session. A session is a predefinedtracking period of time (e.g., 10 minutes). The generated unifieduser-level data for all users are saved in the database 116 or anystorage device.

FIG. 5 is an exemplary and non-limiting flowchart 500 illustrating amethod for generating a unified user-level data according to oneembodiment. At S510, user-level data are received from a plurality ofmedia platforms. The format and representations of the user-level datais different from one platform to another. In an embodiment, theuser-level data includes a conversation tag. Each instance of user-leveldata is referred to an event that may be generated for any serving of anonline advertisement (or any media asset), an interaction of a user withthe online advertisement, and so on. It should be noted that thereceived events are respective of different users. A user-level dataevent typically includes a timestamp (e.g., 17/October 18:01:30), a userID which is a sequence of characters (e.g., de305d54-75b4), an IPaddress of the user device, a user agent, and at least one eventparameter. Such parameters may define any information related to thelocation of the online advertisement (e.g., contact us page) or actionperformed by the user.

At S520 through S550 a process to detect all events related to the sameuser is performed. Specifically, for each received event, at S520, eachevent is analyzed to provide metadata on each event. For example, the IPaddress designated in each received event is decoded to identify thegeographical location of the user, the user agent is decoded todetermine the browser type, operating system time, and so on. In anembodiment, third party data providers 401 are queried to obtainadditional information.

At S530, a similarity vector is created for each analyzed event. Thevector includes the data contained in the event and the additionalmetadata. In an exemplary embodiment, a similarly vector may include thedata fields F1, F2, . . . , FN, each such field containing a piece ofdata from the received event or the metadata. For example, F1 mayinclude the timestamp, F2 may include the IP address, and F3 thedetermined geographical location.

At S540, a similarity score is computed by comparing generatedsimilarity vectors to each other. The similarity score may be a weightedsum computed over data fields of two or more vectors. For example, anoperation system type may have a lower weight as millions of users canuse the same operating system (e.g., iOS®), an IP address may have ahigher weight as two or more users may not have the same IP address atthe same time frame, and a timestamp may have a lower weight as the sameuser is not likely to access the same advertisement at the exact sametime. That is, in an exemplary and non-limiting embodiment, the weightdetermines a certain value that a respective field, if matched betweentwo or more vectors, will indicate on similarity of the vector.According to this embodiment, a high similarity score indicates onevents (vectors) of the same user.

At S550, similarity vectors having a similarity score above a predefinedthreshold are unified and a unique virtual user identifier (ID) isassigned. In an embodiment, the assigned user identifier is a GloballyUnique Identifier (GUID). The GUID is unique reference number used as anidentifier, typically containing 32-hexadecimal digits.

At S560, a sessionization process is performed to detect all events thatdo not belong to a current session. As noted above, a session is apredefined time period. This step is performed as some received eventsmay be a continuation of the previous sessions. In an embodiment, S560includes determining a session period (e.g., 5 minutes). Then, allevents belonging to the same unified user-level data are grouped bytheir GUID (or the virtual ID). The timestamp of each event is matchedto the current time to determine if the elapsed time is more than onesession period. If so, the event belongs to a previous session;otherwise, the event is of the current session and a session identifiedis increased.

At S570, events resulting from the same user are output organized bytheir sessions' IDs. In an embodiment, the unified user-level data of auser may include the respective session IDs. At S580, the unifieduser-level data generated for each user is saved, for example, in thedatabase 316.

FIG. 6 illustrates the process for tracking post-impression andpost-click conversion according to an exemplary embodiment. At S601, auser 610 (e.g., by means of a web browser installed on the clientdevice) accesses a publisher webpage hosted in a publisher web site 612.In an embodiment, this webpage contains an advertisement placement beingtracked by the media link module 112 by means of the unified trackingserver 114.

At S602 the user's browser calls an advertisement server 614 anddisplays the advertisement. At S603, the unified tracking server 114records the impression (a measure of the number of times theadvertisement is displayed on the browser of the user 610). At S604, asthe user clicks on the advertisement, the browser navigates to anadvertiser website 616. The advertiser website 616 contains conversiontags assigned by the media link module 112.

At S605, the browser of the end-user 610 calls the unified trackingserver 114 in order to count the conversion as reported by theconversion tags included in the advertiser website 616. The format ofthe conversion tag is discussed below. In order to track post-impressionand post-click conversions on the advertiser's website 616, the unifiedtracking server 114 is required to embed conversion tags. In oneexemplary embodiment, the proprietary conversion tags use the sameclient's root domain name which allows the ability to show a full pathto conversion including all advertisements from all the differentvendors (due to the fact that they will share the same top leveldomain).

At S606, the unified tracking server 114 in the media link module 112stores the tracking user-level data in the database 116. In anembodiment, the tracking user-level data is a data structure thatcontains information about a user associated with a unique virtual useridentifier. In an embodiment, the tracking user-level data may be in aformat of the unified user-level data as described above, where thevirtual user identifier is a GUID.

In an embodiment, in order to tag end-users (ad viewers) globally acrossdifferent advertising platforms, a new domain name and data structureare disclosed. In one embodiment, the tracking domain name included inthe media link module 112 conversion tag will issue to a user a newdomain name which is a sub-domain of the respective client domain name.A client may be, but is not limited to, an advertiser, a marketer, andan advertisement agency.

The disclosed data structure includes a user ID cookie, which contains aunique identifier of the end-user. In one embodiment, the structure ofthe user-level data structure utilized by media link is as defined asfollows:

-   -   Name:    -   Value: Globally unique identifier (GUID)    -   Path: /    -   Expires: 31 Dec. 2040 00:00:00 GMT

It should be noted that the cookie is added on the top level domain(.acme.com), thereby the data can be shared at the sub-domain level.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the invention and the concepts contributed by the inventor tofurthering the art, and are to be construed as being without limitationto such specifically recited examples and conditions. Moreover, allstatements herein reciting principles, aspects, and embodiments of theinvention, as well as specific examples thereof, are intended toencompass both structural and functional equivalents thereof.Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture, i.e., any elements developed that perform the same function,regardless of structure.

What is claimed is:
 1. A system for tracking users across a plurality ofmedia platforms, comprising: a storage; a processor; and a memory, thememory containing instructions that, when executed by the processor,configure the system to: generate unified user-level data of each useracross the media of advertising platforms; store the unified user-leveldata generated for each user in a storage; tap into the plurality ofadvertising platforms to render pixel trafficking data; and analyze rawdata based on which the unified user-level data is generated.
 2. Thesystem of claim 1, wherein the system is further configured to: trackpost-impression and post-click conversion related to a user activity onat least an advertiser website.
 3. The system of claim 2, wherein thesystem is further configured to: track the track post-impression and thepost-click conversion using at least conversion tags.
 4. The system ofclaim 1, wherein the system is further configured to: create conversiontags; and update a media plan of a respective advertiser website to callthe proprietary tracking tags in order to capture changes in the mediaplan and tracking user activity.
 5. The system of claim 1, wherein thesystem is further configured to: connect to each of the plurality of themedia platforms; extract the media plan of each of the plurality of theadvertising platforms; learn various entities of the extracted mediaplan; and map the extracted media plan into a predefined media plan. 6.The system of claim 1, wherein the system is further configured to:receive user-level data events from the plurality of media platforms,wherein each event relates to at least one online advertisement viewedby a plurality of users; analyze the received user-level data events todetect a group of user-level data events related to the same user of theplurality of users; combine user-level data from each group ofuser-level data events related to the same user; assign a unique useridentifier to the combined user-level data to result in the unifieduser-level data related to a particular user; and store the unifieduser-level data in the database, thereby providing consistent user-leveldata across the plurality of media platforms.
 7. The system of claim 6,wherein the system is further configured to: perform a sessionizationprocess to detect all user-level data events in each group of user-leveldata events that do not belong to a current session.
 8. The system ofclaim 7, wherein the system is further configured to: assign a sessionidentifier for each user-level data event in the group of user-leveldata events based on the sessionization process.
 9. The system of claim1, wherein each of the plurality of media platform is any one of: anad-serving system of an ad-serving company, a social media website, anadvertiser website, and a content publisher.
 10. A method for trackingusers across a plurality of media platforms, comprising: generatingunified user-level data of each user across the media of advertisingplatforms; storing the unified user-level data generated for each userin a storage; taping into the plurality of advertising platforms torender pixel trafficking data; and analyzing raw data based on which theunified user-level data is generated.
 11. The method of claim 10,further comprising: tracking post-impression and post-click conversionrelated to a user activity on at least an advertiser website.
 12. Themethod of claim 11, further comprising: tracking the trackpost-impression and the post-click conversion using at least conversiontags.
 13. The method of claim 10, further comprising: creatingconversion tags; and updating a media plan of a respective advertiserwebsite to call the proprietary tracking tags in order to capturechanges in the media plan and tracking user activity.
 14. The method ofclaim 10, further comprising: connecting to each of the plurality of themedia platforms; extracting the media plan of each of the plurality ofthe advertising platforms; learning various entities of the extractedmedia plan; and mapping the extracted media plan into a predefined mediaplan.
 15. The method of claim 10, further comprising: receivinguser-level data events from the plurality of media platforms, whereineach event relates to at least one online advertisement viewed by aplurality of users; analyzing the received user-level data events todetect a group of user-level data events related to the same user of theplurality of users; combining user-level data from each group ofuser-level data events related to the same user; assigning a unique useridentifier to the combined user-level data to result in the unifieduser-level data related to a particular user; and storing the unifieduser-level data in the database, thereby providing consistent user-leveldata across the plurality of media platforms.
 16. The method of claim15, further comprising: performing a sessionization process to detectall user-level data events in each group of user-level data events thatdo not belong to a current session.
 17. The method of claim 16, furthercomprising: assigning a session identifier for each user-level dataevent in the group of user-level data events based on the sessionizationprocess.
 18. The method of claim 10, wherein each of the plurality ofmedia platform is any one of: an ad-serving system of an ad-servingcompany, a social media website, an advertiser website, and a contentpublisher.
 19. A non-transitory computer readable medium having storedthereon instructions for causing a processor to perform a process fortracking users across a plurality of media platforms, the processcomprising: generating unified user-level data of each user across themedia of advertising platforms; storing the unified user-level datagenerated for each user in a storage; taping into the plurality ofadvertising platforms to render pixel trafficking data; and analyzingraw data based on which the unified user-level data is generated.